Abstract
Current in-field methods for grading logs are based on visual rating scales, which are subjective, operator-dependent and time-consuming. Various wood defects such as knots, resin pockets, rot and compression wood, amongst others, affect the quality and potential usage of a log. Early detection of these defects and an adequate wood quality classification help to optimise resource use along the whole production chain. Therefore, the specific target for the development of an efficient in-field grading approach was defined within the project Integrated processing and controL systems fOr sustainable forest Production in mountain areas – SLOPE. The grading is conducted by means of automatic measurements of selected wood properties with diverse sensors, including near infrared (NIR) spectrometers. A series of studies was conducted on wooden discs using laboratory equipment and a portable NIR spectrometer. In-field measurements of standing trees and harvested logs were also performed using a portable instrument. Principal components analysis models for identification of log defects were developed using the spectra collected with both instruments. Such models will serve for the automated determination of quality indexes to be used for log grading. It is foreseen that the NIR-based quality indexes will be integrated with the expert system under development within the SLOPE project and combined with quality information derived from other sensors. The overall goal is to provide a reliable technology for automatic log quality grading in the forest industry.
© 2016 The Author(s)
PDF Article
More Like This
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription
Add to BibSonomy